Privacy annotation from differential analysis of snapshots

ABSTRACT

A method is provided for preventing divulgation of sensitive data in two snapshots, taken at different times, of one or more same systems in a cloud environment. The method includes identifying a set of files from among a plurality of file pairs. Each file pair is formed from a respective file that includes at least one difference with respect to each snapshot. The method includes performing a pattern reducing process that removes, from the set of files, any of the files having, as the difference, a predetermined non-sensitive difference between respective executions of a pre-determined system operation. The method includes performing a commonality reducing process that removes, from the set of files, any files having, as the difference, a common difference between different users. The method includes annotating data in remaining files in the set of files as potentially being the sensitive data, subsequent to the reducing processes.

BACKGROUND Technical Field

The present invention relates generally to information processing and,in particular, to privacy annotation from differential analysis ofsnapshots.

Description of the Related Art

Recent advances in virtualization technology allows users to supportsystem flexibly by keeping an interim state of the system as an imageover time, and by rolling back the deployed system to a previous stateusing a retained past image.

In DevOps scenarios, users tend to use the same image multiple times toshorten the time for deployment and initial configuration.

Images could include the whole system state, including sensitiveinformation such as passwords and system confidential unique parameters.Such sensitive information needs to be removed from images beforesharing them with others. However, achieving a perfect removal ofsensitive information is often very time-consuming, and/or difficult tocomplete manually.

Thus, there is a need for identifying small differences from adifferential analysis of a snapshot, e.g., in order to remove theaforementioned sensitive information from the snapshot.

SUMMARY

According to an aspect of the present invention, a computer-implementedmethod is provided for preventing divulgation of sensitive data in twosnapshots, taken at different times, of one or more same systems in acloud environment. The method includes identifying a set of files fromamong a plurality of file pairs. Each of the plurality of file pairs isformed from a respective file that includes at least one difference withrespect to each of the two snapshots. The method further includesperforming a pattern reducing process that removes, from the set offiles, any of the files having, as the at least one difference, apredetermined non-sensitive difference between respective executions ofa pre-determined system operation. The method also includes performing acommonality reducing process that removes, from the set of files, any ofthe files having, as the at least one difference, a common differencebetween different system users. The method additionally includesannotating data in remaining ones of the files in the set of files aspotentially being the sensitive data, subsequent to the pattern reducingand commonality reducing processes.

According to another aspect of the present invention, a computer programproduct is provided for preventing divulgation of sensitive data in twosnapshots, taken at different times, of one or more same systems in acloud environment. The computer program product has a non-transitorycomputer readable storage medium having program instructions embodiedtherewith. The program instructions are executable by a computer tocause the computer to perform a method. The method includes identifyinga set of files from among a plurality of file pairs, each of theplurality of file pairs being formed from a respective file thatincludes at least one difference with respect to each of the twosnapshots. The method further includes performing a pattern reducingprocess that removes, from the set of files, any of the files having, asthe at least one difference, a predetermined non-sensitive differencebetween respective executions of a pre-determined system operation. Themethod also includes performing a commonality reducing process thatremoves, from the set of files, any of the files having, as the at leastone difference, a common difference between different system users. Themethod additionally includes annotating data in remaining ones of thefiles in the set of files as potentially being the sensitive data,subsequent to the pattern reducing and commonality reducing processes.

According to yet another aspect of the present invention, a system isprovided for preventing divulgation of sensitive data in two snapshots,taken at different times, of one or more same systems in a cloudenvironment. The system includes one or more processors. The one or moreprocessors are configured to identify a set of files from among aplurality of file pairs, each of the plurality of file pairs beingformed from a respective file that includes at least one difference withrespect to each of the two snapshots. The one or more processors arefurther configured to perform a pattern reducing process that removes,from the set of files, any of the files having, as the at least onedifference, a predetermined non-sensitive difference between respectiveexecutions of a pre-determined system operation. The one or moreprocessors are also configured to perform a commonality reducing processthat removes, from the set of files, any of the files having, as the atleast one difference, a common difference between different systemusers. The one or more processors are additionally configured toannotate data in remaining ones of the files in the set of files aspotentially being the sensitive data, subsequent to said patternreducing and commonality reducing processes.

These and other features and advantages will become apparent from thefollowing detailed description of illustrative embodiments thereof,which is to be read in connection with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The following description will provide details of preferred embodimentswith reference to the following figures wherein:

FIG. 1 shows an exemplary processing system to which the presentinvention may be applied, in accordance with an embodiment of thepresent invention;

FIG. 2 shows an exemplary environment to which the present invention canbe applied, in accordance with an embodiment of the present invention;

FIG. 3 shows an exemplary method for snapshot difference processing, inaccordance with an embodiment of the present invention;

FIG. 4 shows another exemplary method for computing the differencebetween a snapshot of a (sandbox or an actual) system at time T and atT+ΔT, in accordance with an embodiment of the present invention;

FIGS. 5-7 show another exemplary method for snapshot differenceprocessing, in accordance with an embodiment of the present invention.

FIG. 8 shows an exemplary cloud computing environment, in accordancewith an embodiment of the present invention; and

FIG. 9 shows an exemplary set of functional abstraction layers providedby the cloud computing environment shown in FIG. 8, in accordance withan embodiment of the present invention.

DETAILED DESCRIPTION

The present invention is directed to privacy annotation fromdifferential analysis of snapshots.

In an embodiment, the present invention is deployed in an environmenthaving multiple hosts, and a data store. Information of selected files(interchangeably referred to herein as a “snapshot”) on each host ismaintained (stored) periodically by the data store. A snapshot caninclude, for example, a file name and/or file attributes including, butnot limited to, a digest value (hash) of file contents of the selectedfiles. The file selections can be predefined.

In an embodiment, the present invention can be applied to a set of knownnormal server operations and corresponding information including, butnot limited to, for example: package installation; and typical serverconfiguration change information such as a Secure Shell (SSH) serverconfiguration.

FIG. 1 shows an exemplary processing system 100 to which the inventionprinciples may be applied, in accordance with an embodiment of thepresent invention. The processing system 100 includes at least oneprocessor (CPU) 104 operatively coupled to other components via a systembus 102. A cache 106, a Read Only Memory (ROM) 108, a Random AccessMemory (RAM) 110, an input/output (I/O) adapter 120, a sound adapter130, a network adapter 140, a user interface adapter 150, and a displayadapter 160, are operatively coupled to the system bus 102.

A first storage device 122 and a second storage device 124 areoperatively coupled to system bus 102 by the 1/O adapter 120. Thestorage devices 122 and 124 can be any of a disk storage device (e.g., amagnetic or optical disk storage device), a solid state magnetic device,and so forth. The storage devices 122 and 124 can be the same type ofstorage device or different types of storage devices.

A speaker 132 is operatively coupled to system bus 102 by the soundadapter 130. A transceiver 142 is operatively coupled to system bus 102by network adapter 140. A display device 162 is operatively coupled tosystem bus 102 by display adapter 160.

A first user input device 152, a second user input device 154, and athird user input device 156 are operatively coupled to system bus 102 byuser interface adapter 150. The user input devices 152, 154, and 156 canbe any of a keyboard, a mouse, a keypad, an image capture device, amotion sensing device, a microphone, a device incorporating thefunctionality of at least two of the preceding devices, and so forth. Ofcourse, other types of input devices can also be used, while maintainingthe spirit of the present invention. The user input devices 152, 154,and 156 can be the same type of user input device or different types ofuser input devices. The user input devices 152, 154, and 156 are used toinput and output information to and from system 100.

Of course, the processing system 100 may also include other elements(not shown), as readily contemplated by one of skill in the art, as wellas omit certain elements. For example, various other input devicesand/or output devices can be included in processing system 100,depending upon the particular implementation of the same, as readilyunderstood by one of ordinary skill in the art. For example, varioustypes of wireless and/or wired input and/or output devices can be used.Moreover, additional processors, controllers, memories, and so forth, invarious configurations can also be utilized as readily appreciated byone of ordinary skill in the art. These and other variations of theprocessing system 100 are readily contemplated by one of ordinary skillin the art given the teachings of the present invention provided herein.

Moreover, it is to be appreciated that environment 200 described belowwith respect to FIG. 2 is an environment for implementing respectiveembodiments of the present invention. Part or all of processing system100 may be implemented in one or more of the elements of environment200.

Further, it is to be appreciated that processing system 100 may performat least part of the methods described herein including, for example, atleast part of method 300 of FIG. 3 and/or at least part of method 400 ofFIG. 4 and/or at least part of method 500 of FIGS. 5-7. Similarly, partor all of environment 200 may be used to perform at least part of method300 of FIG. 3 and/or at least part of method 400 of FIG. 4 and/or atleast part of method 500 of FIGS. 5-7.

FIG. 2 shows an exemplary environment 200 to which the present inventioncan be applied, in accordance with an embodiment of the presentinvention.

The environment 200 includes a set of computing systems, interchangeablyreferred to as “hosts” or “computing devices”, and collectively andsingularly denoted by the figure reference numeral 210. The set ofcomputing systems 210 are configured using a cloud configuration.Exemplary cloud configurations are described herein below.

The set of computing systems 210 includes actual computing systems210A-210C (e.g., servers, etc.) and a computing system 210D. Thecomputing system 210D is configured to include a Sandbox host 290 havingone or more Sandboxes (hereinafter “Sandbox” in short) 291.

As is known, a Sandbox host is a host on which computer program code canbe tested while protecting a “live” computing system and its data fromchanges that could be damaging and/or unwanted. It is to be appreciatedthat the phrases “live computing system” and “actual computing system”are used interchangeably herein.

The present invention involves privacy annotation from differentialanalysis of snapshots. The snapshots can be of one or more of any of thecomputing systems 210.

Various steps of the methods described herein can be performed using theset of computing systems 210 in different configurations, as furtherdescribed herein below. For example, some steps of the methods can beperformed by and/or otherwise relate to actual computing systems (e.g.,actual computing systems 210A-210C) while other steps of the methods canbe performed by and/or otherwise relate to a Sandbox (e.g., Sandbox291). It is to be appreciated that other configurations are readilyimplemented by one of ordinary skill in the art given the teachings ofthe present invention provided herein, while maintaining the spirit ofthe present invention.

The environment 200 further includes a data store 250 that is accessibleby the set of computing systems 210. The data snapshots and/or otherdata pertinent to the present invention can be stored in the data store250. In an embodiment, the data store 250, or a different memory device,is used to store predetermined differences that can be used in thepattern reducing process described in detail herein below. In this way,efficient annotation of sensitive information can be achieved. In theexample of FIG. 2, the data store 250 is implemented as a standalonedevice. However, in other embodiments, the data store can be included inone or more of the computing systems 210.

In the embodiment shown in FIG. 2, the elements thereof areinterconnected by a network(s) 201. However, in other embodiments, othertypes of connections can also be used. Moreover, in an embodiment, atleast one of the elements of system 200 is processor-based. Further,while one or more elements may be shown as separate elements, in otherembodiments, these elements can be combined as one element. The converseis also applicable, where while one or more elements may be part ofanother element, in other embodiments, the one or more elements may beimplemented as standalone elements. Moreover, one or more elements ofFIG. 2 can be implemented in a cloud configuration including, forexample, in a distributed configuration. Additionally, one or moreelements in FIG. 2 may be implemented by a variety of devices, whichinclude but are not limited to, Digital Signal Processing (DSP)circuits, programmable processors, Application Specific IntegratedCircuits (ASICs), Field Programmable Gate Arrays (FPGAs), ComplexProgrammable Logic Devices (CPLDs), and so forth. These and othervariations of the elements of system 200 are readily determined by oneof ordinary skill in the art, given the teachings of the presentinvention provided herein, while maintaining the spirit of the presentinvention.

FIG. 3 shows an exemplary method 300 for snapshot difference processing,in accordance with an embodiment of the present invention. The snapshotprocessing can include computing smaller, more reduced, more targeteddifferences from actual differences, as further explained in detailherein.

The method 300 can involve multiple Sandbox hosts, multiple actualcomputing systems, or a mix of Sandbox hosts and actual computingsystems. For the sake of illustration, various selections have been madebelow regarding which of (i) Sandbox hosts or (ii) actual computingsystems, will be performing a particular step of method 300. However,these selections can be varied depending upon the implementation, asreadily appreciated by one of ordinary skill in the art, given theteachings of the present invention provided herein.

At step 310 (preparation on Sandbox hosts), compute a (well-known)difference set generated by each normal operation in a set of normaloperations (performed on Sandbox hosts).

At step 320 (performed on each actual computing system), compute anactual difference set (of actual differences) generated by actualoperations.

At step 330 (performed on each actual computing system), compute atarget difference set (of target differences) by removing all of thewell-known differences from the actual differences (that is, bysubtracting the set of well-known differences from the set of actualdifferences).

At step 340 (performed on multiple actual computing systems), find acommon set in the target differences from the multiple systems. That is,find a common set (of differences) in the target differences set of eachof the multiple systems.

At step 350 (performed on each actual computing system), compute asmaller difference set by removing the common set from the targetdifference, and then set the smaller difference as a (refined) targetdifference.

At step 360, iterate between steps 340 and 350 until a common set (inthe target differences from the multiple systems) cannot be found instep 340.

At step 370, output, for each of the actual computing systems, a mostrefined (i.e., from the last “successful iteration”) target difference(after the iterations between steps 340 and 350).

FIG. 4 shows an exemplary method 400 for computing the differencebetween a snapshot of a (sandbox or an actual) system at time T and atT+ΔT, in accordance with an embodiment of the present invention.

At step 410, take a snapshot of sandbox at time T and store it in thedata store.

At step 420, take a snapshot of sandbox at time T+ΔT and store it in thedata store.

At step 430, compute the difference between the two snapshots byidentifying files such that (i) the pair has different attributes, (ii)the pair has different hash values, (iii) the file has been added ordeleted. In an embodiment, the difference computed at step 430 canrelate to an operation that is performed between steps 410 and 420 (and,thus, is included in the second snapshot of step 420 but not the firstsnapshot of step 410).

A description will now be given regarding various difference operations,in accordance with an embodiment of the present invention.

Initially, the following difference operation is described: computedifference B by reducing X from other difference A, where difference Aand difference B are the list of files (e.g., file names and/or fileattributes and/or file bash values). In an embodiment, “computedifference B by reducing X from difference A” means compute the list offiles each of which is not present in X but is present in A and then setthe list of files as difference B. This is used to compute smallerdifference from others.

Next, the following difference operation is described: find a common setin the differences (A1, A2, . . . , An). Each of the differences (A1, .. . , An) is a list of files (e.g., file names and/or file attributesand/or file hash values). In an embodiment, “find a common set in thedifferences (A1, A2, . . . , An)” means:

(a) finding a relevant set of differences using: (a.i) similarity ofimages (e.g., using image data such as Operating System (OS) data,distribution data, creation data, updating data, and so forth); and(a.ii) relationship of images (e.g., using image metadata such as imagehistory (e.g. Docker history), and so forth);(b) finding the list of files all of which are commonly included in someset of differences; and(c) making the list of files a member of the common set.

The common set (in the differences A1, A2, . . . , An) is used tocompute untargeted/commonly found parts of a difference and/or anuntargeted/commonly found difference.

FIGS. 5-7 show another exemplary method 500 for snapshot differenceprocessing, in accordance with an embodiment of the present invention.The method 500 can be used, for example, to prevent divulgation ofsensitive data in two snapshots, taken at different times (e.g., time Tand at time T+ΔT), of one or more same systems in a cloud environment.

In an embodiment, the two snapshots include at least one Virtual Machine(VM) image of the one or more same systems. In an embodiment, the twosnapshots include at least one Sandbox-based image of the one or moresame systems. In an embodiment, the two snapshots include at least oneSandbox-based image and at least one actual-system-based image of theone or more same systems.

At step 510, identify a set of files from among multiple file pairs.Each file pair is formed from a respective file that includes at leastone difference with respect to each of the two snapshots. The at leastone difference can include, but is not limited to, (i) different fileattributes, (ii) different file hash values, and (iii) a status of oneof the respective files being added or deleted relative to the other oneof the respective files in a given file pair.

At step 520, perform a pattern reducing process that removes, from theset of files (initially identified at step 510), any of the fileshaving, as the at least one difference, a predetermined non-sensitivedifference between respective executions of a pre-determined systemoperation.

At step 530, perform a commonality reducing process that removes, fromthe set of files (initially identified at step 510 and potentiallyreduced by step 520), any files having, as the at least one difference,a common difference between different system users. In an embodiment,the common difference between the different system users can bedetermined based on data that include, but is not limited to, imagecontent similarity data and image relationship data. In an embodiment,the image content similarity data can include, but is not limited to,operating system data, distribution data, file creation data, fileupdate data, and so forth. In an embodiment, the image relationship datacan include, but is not limited to, meta-data derived image history data(e.g., Docket data).

At step 540, iterate between the pattern reducing process of step 520and the commonality reducing process of step 530 based on one or moreiteration criterion. In an embodiment, the one or more iterationcriterion can include an absence of further size reduction in theremaining ones of the files in the set, e.g., after the pattern reducingprocess of step 520.

At step 550 (upon completing iterating between the pattern reducingprocess of step 520 and the commonality reducing process of step 530),annotate data in remaining ones of the files in the set of files aspotentially being the sensitive data. In an embodiment, the data can beannotated using a predetermined set of annotations. For example, in anembodiment, upon data in a remaining one of the files matching a presetlist of data words and/or phrases, corresponding paired annotations canbe used. These and other variations in implementing the annotating stepare readily determined by one of ordinary skill in the art given theteachings of the present invention provided herein, while maintainingthe spirit of the present invention.

At step 560, prompt the user to provide a user input indicating whetherto delete the annotated data.

At step 565, determine whether the user input indicates to delete or notdelete the annotated data. If the user input indicates to delete theannotated data, then proceed to step 570. Otherwise, proceed to step580.

At step 570, delete the annotated data responsive to the user input.

At step 580, check the annotations of the annotated data to generate anannotation checking result. In an embodiment, the annotations can bechecked for correspondence to a set of possible annotations. In anembodiment, the set of possible annotations can relate to one or moresubjects that include, but are not limited to, privacy, security,malware, and so forth. In an embodiment, each of the possibleannotations in the set can be paired with a respective annotationchecking system reaction.

At step 585, determine whether the annotation checking result indicatesa match with a possible annotation. If so, then proceed to step 590.Otherwise, terminate the method.

At step 590, perform the corresponding paired annotation checking systemreaction. In an embodiment, the corresponding paired annotation checkingsystem reaction can include, but is not limited to, modifying a systemconfiguration of one or more systems in the cloud environment. Thesystem configuration modification can involve powering down a system orportion thereof, isolating a system or a portion thereof, reconfiguringa system or portion thereof using a redacted snapshot (with theredaction directed to sensitive information identified by the presentinvention) and so forth.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 8, illustrative cloud computing environment 850 isdepicted. As shown, cloud computing environment 850 includes one or morecloud computing nodes 810 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 854A, desktop computer 8548, laptop computer 854C,and/or automobile computer system 854N may communicate. Nodes 810 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 850 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 854A-Nshown in FIG. 8 are intended to be illustrative only and that computingnodes 810 and cloud computing environment 850 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 9, a set of functional abstraction layers providedby cloud computing environment 850 (FIG. 8) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 9 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 960 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 961;RISC (Reduced Instruction Set Computer) architecture based servers 962;servers 963; blade servers 964; storage devices 965; and networks andnetworking components 966. In some embodiments, software componentsinclude network application server software 967 and database software968.

Virtualization layer 970 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers971; virtual storage 972; virtual networks 973, including virtualprivate networks; virtual applications and operating systems 974; andvirtual clients 975.

In one example, management layer 980 may provide the functions describedbelow. Resource provisioning 981 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 982provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 983 provides access to the cloud computing environment forconsumers and system administrators. Service level management 984provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 985 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 990 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 991; software development and lifecycle management 992;virtual classroom education delivery 993; data analytics processing 994;transaction processing 995; and privacy annotation 996.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Reference in the specification to “one embodiment” or “an embodiment” ofthe present invention, as well as other variations thereof, means that aparticular feature, structure, characteristic, and so forth described inconnection with the embodiment is included in at least one embodiment ofthe present invention. Thus, the appearances of the phrase “in oneembodiment” or “in an embodiment”, as well any other variations,appearing in various places throughout the specification are notnecessarily all referring to the same embodiment.

It is to be appreciated that the use of any of the following “/”,“and/or”, and “at least one of”, for example, in the cases of “A/B”, “Aand/or B” and “at least one of A and B”, is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of both options (A andB). As a further example, in the cases of “A, B, and/or C” and “at leastone of A, B, and C”, such phrasing is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of the third listedoption (C) only, or the selection of the first and the second listedoptions (A and B) only, or the selection of the first and third listedoptions (A and C) only, or the selection of the second and third listedoptions (B and C) only, or the selection of all three options (A and Band C). This may be extended, as readily apparent by one of ordinaryskill in this and related arts, for as many items listed.

Having described preferred embodiments of a system and method (which areintended to be illustrative and not limiting), it is noted thatmodifications and variations can be made by persons skilled in the artin light of the above teachings. It is therefore to be understood thatchanges may be made in the particular embodiments disclosed which arewithin the scope of the invention as outlined by the appended claims.Having thus described aspects of the invention, with the details andparticularity required by the patent laws, what is claimed and desiredprotected by Letters Patent is set forth in the appended claims.

The invention claimed is:
 1. A computer-implemented method forpreventing divulgation of sensitive data in two snapshots, taken atdifferent times, of one or more same systems in a cloud environment, themethod comprising: identifying, by a hardware processor, a set of filesfrom among a plurality of file pairs, each of the plurality of filepairs being formed from a respective file that includes at least onedifference with respect to each of the two snapshots, wherein the twosnapshots comprise at least one Virtual Machine (VM) image of the one ormore same systems of the cloud environment; performing, by the hardwareprocessor, a pattern reducing process that removes, from the set offiles having, as the at least one difference, a predeterminednon-sensitive difference between respective executions of apre-determined system operation; performing, by the hardware processor,a commonality reducing process that removes, from the set of files, anyof the files having, as the at least one difference, a common differencebetween different system users; and modifying, by the hardwareprocessor, a system configuration of at least one of the one or moresame systems, responsive to the annotation checking result.
 2. Thecomputer-implemented method of claim 1, further comprising: promptingthe user to provide a user input indicating whether to delete theannotated data; and deleting the annotated data responsive to the userinput.
 3. The computer-implemented method of claim 1, furthercomprising: checking annotations of the annotated data to generate anannotation checking result.
 4. The computer-implemented method of claim1, wherein the two snapshots comprise at least one Sandbox-based imageof the one or more same systems of the cloud environment.
 5. Thecomputer-implemented method of claim 1, wherein the two snapshotscomprise at least one Sandbox-based image and at least oneactual-system-based image of the one or more same systems of the cloudenvironment.
 6. The computer-implemented method of claim 1, wherein eachof the plurality of file pairs is formed based on the respective filestherein having the at least one difference there between selected fromthe group consisting of (i) different attributes, (ii) different hashvalues and (iii) a status of one of the respective files being added ordeleted relative to the other one of the respective files in a given oneof the file pairs.
 7. The computer-implemented method of claim 1,wherein the common difference between the different system users isdetermined based on image content similarity data and image relationshipdata.
 8. The computer-implemented method of claim 7, wherein the imagecontent similarity data is selected from the group consisting ofoperating system data, distribution data, file creation data, and fileupdate data.
 9. The computer-implemented method of claim 7, wherein theimage relationship data comprises meta-data derived image history data.10. The computer-implemented method of claim 1, wherein thepredetermined non-sensitive difference between the respective executionsof the pre-determined system operation is determined using a Sandboxhost, and wherein the common difference between the different systemusers is determined using an actual one of the one or more systems. 11.The computer-implemented method of claim 1, wherein the commonalityreducing process and the pattern reducing process are iterativelyperformed based on one or more iteration criterion.
 12. Thecomputer-implemented method of claim 11, wherein the one or moreiteration criterion comprise an absence of further size reduction in theremaining ones of the files in the set.